Eurekahedge and Mizuho-DL Financial Technology have partnered to deliver a customised alpha measure. The Eurekahedge Systematic Alpha (ESA) runs on a fund level across the entire breadth of the Eurekahedge Global Hedge Fund Database.

The ESA statistic is a systematic approach to measuring the excess returns or alpha of a hedge fund, and is available for display on individual fund factsheets on the Eurekahedge platform as well within the multitude of Eurekahedge hedge fund databases.

As part of the roll out of the ESA statistic on a fund level basis, Eurekahedge will through its proprietary methodology identify and short-list the top ranking alpha funds as potential constituents for the new Eurekahedge Systematic Alpha Hedge Fund Indexes .The Eurekahedge Systematic Alpha (ESA) methodology is as follows.

In recent years, academic empirical studies have shown that representative hedge fund indices can be replicated with some stylised, tradable systematic risk factors at the range from 40% to 80% of the risks (variances) of the indices. This implies that a part of hedge fund returns can be captured through systematic exposure to various risk factors, the range for which has varied across empirical studies on the following accounts:

Despite the aforementioned challenges, it has been concluded based on empirical research coming out of these multi-factor regression models that hedge fund returns can be decomposed into traditional beta, alternative beta and the residual or unexplained portion – the manager’s alpha.

However, given the nature of actively managed investments such as hedge funds, not all sources of excess returns can be replicated with tradable risk factors. This implies that alpha which may be attributed to the manager’s skill could very likely be that portion of alternative beta which cannot be effectively modelled or replicated.

Despite this limitation, a systematic approach to quantifying alpha offers investors the opportunity to identify and access hedge funds with exotic sources of returns that would help diversify their portfolios. Furthermore, the prospect of successfully modelling a hedge fund manager’s systematic drivers of returns (i.e. alternative beta) gives investors the opportunity to access a part of the fund manager’s return at a lower cost through either replicator indices or via synthetic products.

With this in mind, Eurekahedge has launched the first of its kind ‘pure alpha information’ on a fund level across the entire breadth of our hedge fund database covering all geographic and strategic mandates. This information, as captured by the Eurekahedge Systematic Alpha (ESA) statistic allows investors to identify, rank and compare individual hedge funds based on their ability to deliver excess returns or pure alpha.

Methodology

The Eurekahedge Systematic Alpha (ESA) utilises multifactor regression analysis against seven pre-specified common risk factors to assign an alpha value to individual hedge funds based on the manager’s ability to deliver returns in excess of the systematic risk factors modelled. The constant term in the regression analysis is recognised as the ESA statistic for an individual fund.

The seven risk factors consist of

Market beta, Value, Size (World equities)

High-yield spread (Low/high investment grade)

Term spread (US treasuries)

Momentum (World Equities)

Liquidity

Returns are regressed against the specified risk factors over a 36-month period to assign ESA values to individual hedge funds.

Based on the ESA values, funds are subsequently ranked across the breadth of the Eurekahedge Global Hedge Fund Database.

Table 1 below shows how funds with a positive Eurekahedge Systematic Alpha (ESA) value have outperformed their peers since 2013.

As the first of its kind systematic approach to assigning alpha values on the individual fund level basis (as opposed to the more ubiquitous index level analysis), the ESA statistic’s investor friendly appeal should be viewed with caution for some of the main reasons listed below.

The list of risk factors employed in the calculation of the ESA is by no means exhaustive and/or perfectly representative taking into account a fund’s unique strategy, regional exposure and overall approach to risk taking as well as risk mitigation.

The 36-month look back window utilised in the calculation of the ESA yields an ‘alpha value’ that will not adequately represent a manager’s historical ability to capture excess returns during the various market cycles.

ESA values may be misleading on account of model misspecification at the individual fund level, with unaccounted alternative beta posturing as pure alpha.

The approach suffers from the common pitfalls for any multifactor regression analysis, and for detailed analysis individual factors as well as the overall explanatory power of the model should be subjected to significance tests among others.

Table 2: A systematic approach to quantifying alpha has limitations

Year

% of funds with negative ESA values which outperform the average returns posted by positive ESA funds*#

2013

27.89%

2014

19.64%

2015

15.42%

2016 September

29.63%

ESA rankings; which are intended as a guide towards identifying managers with exotic sources of alpha, should be viewed in relation to the manager’s full track record, and are intended more as a helpful guide to the manager’s recent performance against common market risk factors, as opposed to a seal of approval (or disapproval) on their ability (or lack thereof) to deliver excess returns.

For more information on Eurekahedge indices, please contact us on +1 212 706 7020 (US office) or +65 6212 0925 (Singapore office), or email us at indices@eurekahedge.com.

Footnotes

1 For ESA values as of July 2016

* In AY 2015, 15.42% of the funds with a negative ESA value beat the average return of 4.96% (see Table 1) posted by funds with a positive ESA value.